Multivariate Time-Series

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Clustering Three-Way Data with Outliers
Oct 8, 2023Katharine M. Clark, Paul D. McNicholasOutliers Clustering
Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.
BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification
Date pendingJingtao Zhang, Yi Liu, Qi Shen +1Hybrid Transformer Time-Series Classification
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University · College of Medicine and Biological Information Engineering, Northeastern University

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Clustering Three-Way Data with Outliers

Katharine M. Clark, Paul D. McNicholas

Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.

Outliers Clustering Multivariate Time-Series

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Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dealing with outliers in these models. An approach for clustering matrix-variate normal data with outliers is discussed. The approach, which uses the distribution of subset log-likelihoods, extends the OCLUST algorithm to matrix-variate normal data and uses an iterative approach to detect and trim outliers.

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Latest papers 62

Clustering Three-Way Data with Outliers

BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification